Adaptive rebuilding of encoded data slices in a storage network

ABSTRACT

A method for execution by a computing device of a storage network begins by obtaining scoring information for a rebuilding encoded data slices for one or more storage units of a set of storage units of the storage network, where the scoring information includes two or more of a plurality of rebuilding rates, a plurality of input/output rates, a plurality of scores, and a plurality of selection rates. The method continues with determining a rebuilding rate of the plurality of rebuilding rates to utilize for the rebuilding based on the scoring information. The method continues by implementing the rebuilding of the encoded data slices in accordance with the rebuilding rate.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority pursuant to 35 U.S.C. § 120 as acontinuation of U.S. Utility application Ser. No. 15/823,931, entitled“Accelerated Learning In Adaptive Rebuilding By Applying Observations ToOther Samples”, filed Nov. 28, 2017, which is a continuation-in-part ofU.S. Utility application Ser. No. 14/287,534, entitled “DistributedStorage Network With Client Subsets And Methods For Use Therewith”,filed May 27, 2014, issued on Feb. 13, 2018 as U.S. Pat. No. 9,894,157,which claims priority pursuant to 35 U.S.C. § 119(e) to U.S. ProvisionalApplication No. 61/860,456, entitled “Establishing A Slice RebuildingRate In A Dispersed Storage Network”, filed Jul. 31, 2013, all of whichare hereby incorporated herein by reference in their entirety and madepart of the present U.S. Utility Patent Application for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not Applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not Applicable.

BACKGROUND OF THE INVENTION Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to rebuilding dispersing error encoded data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data in accordance with the present invention;

FIG. 4 is a schematic block diagram of a generic example of an errorencoding function in accordance with the present invention;

FIG. 5 is a schematic block diagram of a specific example of an errorencoding function in accordance with the present invention;

FIG. 6 is a schematic block diagram of an example of a slice name of anencoded data slice (EDS) in accordance with the present invention;

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of data in accordance with the present invention;

FIG. 8 is a schematic block diagram of a generic example of an errordecoding function in accordance with the present invention;

FIG. 9A is a schematic block diagram of another embodiment of adispersed storage network (DSN) system in accordance with the presentinvention;

FIG. 9B is a timing diagram illustrating an example of accessperformance in accordance with the present invention;

FIG. 9C is a flowchart illustrating an example of prioritizing accessrates in accordance with the present invention;

FIG. 10A is a diagram illustrating an example of modifying scoringinformation in accordance with the present invention;

FIG. 10B is a diagram illustrating another example of modifying scoringinformation in accordance with the present invention;

FIG. 10C is a flowchart illustrating an example of updating scoringinformation in accordance with the present invention;

FIG. 11A is a diagram illustrating another example of modifying scoringinformation in accordance with the present invention;

FIG. 11B is a flowchart illustrating another example of updating scoringinformation in accordance with the present invention;

FIG. 12A is a diagram illustrating another example of modifying scoringinformation in accordance with the present invention; and

FIG. 12B is a flowchart illustrating another example of updating scoringinformation in accordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, ordistributed, storage network (DSN) 10 that includes a plurality ofcomputing devices 12-16, a managing unit 18, an integrity processingunit 20, and a DSN memory 22. The components of the DSN 10 are coupledto a network 24, which may include one or more wireless and/or wirelined communication systems; one or more non-public intranet systemsand/or public internet systems; and/or one or more local area networks(LAN) and/or wide area networks (WAN).

The DSN memory 22 includes a plurality of storage units 36 that may belocated at geographically different sites (e.g., one in Chicago, one inMilwaukee, etc.), at a common site, or a combination thereof. Forexample, if the DSN memory 22 includes eight storage units 36, eachstorage unit is located at a different site. As another example, if theDSN memory 22 includes eight storage units 36, all eight storage unitsare located at the same site. As yet another example, if the DSN memory22 includes eight storage units 36, a first pair of storage units are ata first common site, a second pair of storage units are at a secondcommon site, a third pair of storage units are at a third common site,and a fourth pair of storage units are at a fourth common site. Notethat a DSN memory 22 may include more or less than eight storage units36. Further note that each storage unit 36 includes a computing core (asshown in FIG. 2 , or components thereof) and a plurality of memorydevices for storing dispersed error encoded data.

Each of the computing devices 12-16, the managing unit 18, and theintegrity processing unit 20 include a computing core 26, which includesnetwork interfaces 30-33. Computing devices 12-16 may each be a portablecomputing device and/or a fixed computing device. A portable computingdevice may be a social networking device, a gaming device, a cell phone,a smart phone, a digital assistant, a digital music player, a digitalvideo player, a laptop computer, a handheld computer, a tablet, a videogame controller, and/or any other portable device that includes acomputing core. A fixed computing device may be a computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a videogame console, and/or any type of home or office computing equipment.Note that each of the managing unit 18 and the integrity processing unit20 may be separate computing devices, may be a common computing device,and/or may be integrated into one or more of the computing devices 12-16and/or into one or more of the storage units 36.

Each interface 30, 32, and 33 includes software and hardware to supportone or more communication links via the network 24 indirectly and/ordirectly. For example, interface 30 supports a communication link (e.g.,wired, wireless, direct, via a LAN, via the network 24, etc.) betweencomputing devices 14 and 16. As another example, interface 32 supportscommunication links (e.g., a wired connection, a wireless connection, aLAN connection, and/or any other type of connection to/from the network24) between computing devices 12 & 16 and the DSN memory 22. As yetanother example, interface 33 supports a communication link for each ofthe managing unit 18 and the integrity processing unit 20 to the network24.

Computing devices 12 and 16 include a dispersed storage (DS) clientmodule 34, which enables the computing device to dispersed storage errorencode and decode data 40 as subsequently described with reference toone or more of FIGS. 3-8 . In this example embodiment, computing device16 functions as a dispersed storage processing agent for computingdevice 14. In this role, computing device 16 dispersed storage errorencodes and decodes data (e.g., data 40) on behalf of computing device14. With the use of dispersed storage error encoding and decoding, theDSN 10 is tolerant of a significant number of storage unit failures (thenumber of failures is based on parameters of the dispersed storage errorencoding function) without loss of data and without the need for aredundant or backup copies of the data. Further, the DSN 10 stores datafor an indefinite period of time without data loss and in a securemanner (e.g., the system is very resistant to unauthorized attempts ataccessing the data).

In operation, the managing unit 18 performs DS management services. Forexample, the managing unit 18 establishes distributed data storageparameters (e.g., vault creation, distributed storage parameters,security parameters, billing information, user profile information,etc.) for computing devices 12-14 individually or as part of a group ofuser devices. As a specific example, the managing unit 18 coordinatescreation of a vault (e.g., a virtual memory block associated with aportion of an overall namespace of the DSN) within the DSTN memory 22for a user device, a group of devices, or for public access andestablishes per vault dispersed storage (DS) error encoding parametersfor a vault. The managing unit 18 facilitates storage of DS errorencoding parameters for each vault by updating registry information ofthe DSN 10, where the registry information may be stored in the DSNmemory 22, a computing device 12-16, the managing unit 18, and/or theintegrity processing unit 20.

The DSN managing unit 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSN memory 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSTN managing unit 18 tracks the number of times a useraccesses a non-public vault and/or public vaults, which can be used togenerate a per-access billing information. In another instance, the DSTNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generate aper-data-amount billing information.

As another example, the managing unit 18 performs network operations,network administration, and/or network maintenance. Network operationsincludes authenticating user data allocation requests (e.g., read and/orwrite requests), managing creation of vaults, establishingauthentication credentials for user devices, adding/deleting components(e.g., user devices, storage units, and/or computing devices with a DSclient module 34) to/from the DSN 10, and/or establishing authenticationcredentials for the storage units 36. Network administration includesmonitoring devices and/or units for failures, maintaining vaultinformation, determining device and/or unit activation status,determining device and/or unit loading, and/or determining any othersystem level operation that affects the performance level of the DSN 10.Network maintenance includes facilitating replacing, upgrading,repairing, and/or expanding a device and/or unit of the DSN 10.

The integrity processing unit 20 performs rebuilding of ‘bad’ or missingencoded data slices. At a high level, the integrity processing unit 20performs rebuilding by periodically attempting to retrieve/list encodeddata slices, and/or slice names of the encoded data slices, from the DSNmemory 22. For retrieved encoded slices, they are checked for errors dueto data corruption, outdated version, etc. If a slice includes an error,it is flagged as a ‘bad’ slice. For encoded data slices that were notreceived and/or not listed, they are flagged as missing slices. Badand/or missing slices are subsequently rebuilt using other retrievedencoded data slices that are deemed to be good slices to produce rebuiltslices. The rebuilt slices are stored in the DSTN memory 22.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSN interface module 76.

The DSN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). The DSNinterface module 76 and/or the network interface module 70 may functionas one or more of the interface 30-33 of FIG. 1 . Note that the IOdevice interface module 62 and/or the memory interface modules 66-76 maybe collectively or individually referred to as IO ports.

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data. When a computing device 12 or 16 has data tostore it disperse storage error encodes the data in accordance with adispersed storage error encoding process based on dispersed storageerror encoding parameters. The dispersed storage error encodingparameters include an encoding function (e.g., information dispersalalgorithm, Reed-Solomon, Cauchy Reed-Solomon, systematic encoding,non-systematic encoding, on-line codes, etc.), a data segmentingprotocol (e.g., data segment size, fixed, variable, etc.), and per datasegment encoding values. The per data segment encoding values include atotal, or pillar width, number (T) of encoded data slices per encodingof a data segment i.e., in a set of encoded data slices); a decodethreshold number (D) of encoded data slices of a set of encoded dataslices that are needed to recover the data segment; a read thresholdnumber (R) of encoded data slices to indicate a number of encoded dataslices per set to be read from storage for decoding of the data segment;and/or a write threshold number (W) to indicate a number of encoded dataslices per set that must be accurately stored before the encoded datasegment is deemed to have been properly stored. The dispersed storageerror encoding parameters may further include slicing information (e.g.,the number of encoded data slices that will be created for each datasegment) and/or slice security information (e.g., per encoded data sliceencryption, compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as theencoding function (a generic example is shown in FIG. 4 and a specificexample is shown in FIG. 5 ); the data segmenting protocol is to dividethe data object into fixed sized data segments; and the per data segmentencoding values include: a pillar width of 5, a decode threshold of 3, aread threshold of 4, and a write threshold of 4. In accordance with thedata segmenting protocol, the computing device 12 or 16 divides the data(e.g., a file (e.g., text, video, audio, etc.), a data object, or otherdata arrangement) into a plurality of fixed sized data segments (e.g., 1through Y of a fixed size in range of Kilo-bytes to Tera-bytes or more).The number of data segments created is dependent of the size of the dataand the data segmenting protocol.

The computing device 12 or 16 then disperse storage error encodes a datasegment using the selected encoding function (e.g., Cauchy Reed-Solomon)to produce a set of encoded data slices. FIG. 4 illustrates a genericCauchy Reed-Solomon encoding function, which includes an encoding matrix(EM), a data matrix (DM), and a coded matrix (CM). The size of theencoding matrix (EM) is dependent on the pillar width number (T) and thedecode threshold number (D) of selected per data segment encodingvalues. To produce the data matrix (DM), the data segment is dividedinto a plurality of data blocks and the data blocks are arranged into Dnumber of rows with Z data blocks per row. Note that Z is a function ofthe number of data blocks created from the data segment and the decodethreshold number (D). The coded matrix is produced by matrix multiplyingthe data matrix by the encoding matrix.

FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar number (T) of five and decode threshold number of three.In this example, a first data segment is divided into twelve data blocks(D1-D12). The coded matrix includes five rows of coded data blocks,where the first row of X11-X14 corresponds to a first encoded data slice(EDS 1_1), the second row of X21-X24 corresponds to a second encodeddata slice (EDS 2_1), the third row of X31-X34 corresponds to a thirdencoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to afourth encoded data slice (EDS 4_1), and the fifth row of X51-X54corresponds to a fifth encoded data slice (EDS 5_1). Note that thesecond number of the EDS designation corresponds to the data segmentnumber.

Returning to the discussion of FIG. 3 , the computing device alsocreates a slice name (SN) for each encoded data slice (EDS) in the setof encoded data slices. A typical format for a slice name 80 is shown inFIG. 6 . As shown, the slice name (SN) 80 includes a pillar number ofthe encoded data slice (e.g., one of 1-T), a data segment number (e.g.,one of 1-Y), a vault identifier (ID), a data object identifier (ID), andmay further include revision level information of the encoded dataslices. The slice name functions as, at least part of, a DSN address forthe encoded data slice for storage and retrieval from the DSN memory 22.

As a result of encoding, the computing device 12 or 16 produces aplurality of sets of encoded data slices, which are provided with theirrespective slice names to the storage units for storage. As shown, thefirst set of encoded data slices includes EDS 1_1 through EDS 5_1 andthe first set of slice names includes SN 1_1 through SN 5_1 and the lastset of encoded data slices includes EDS 1_Y through EDS 5_Y and the lastset of slice names includes SN 1_Y through SN 5_Y.

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of a data object that was dispersed storage error encodedand stored in the example of FIG. 4 . In this example, the computingdevice 12 or 16 retrieves from the storage units at least the decodethreshold number of encoded data slices per data segment. As a specificexample, the computing device retrieves a read threshold number ofencoded data slices.

To recover a data segment from a decode threshold number of encoded dataslices, the computing device uses a decoding function as shown in FIG. 8. As shown, the decoding function is essentially an inverse of theencoding function of FIG. 4 . The coded matrix includes a decodethreshold number of rows (e.g., three in this example) and the decodingmatrix in an inversion of the encoding matrix that includes thecorresponding rows of the coded matrix. For example, if the coded matrixincludes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2,and 4, and then inverted to produce the decoding matrix.

FIG. 9A is a schematic block diagram of another embodiment of adispersed storage network (DSN) system 900 that includes the distributedstorage and task (DST) integrity processing unit 20, the DST clientmodule 34, the network 24, and the DST execution unit 36 of FIG. 1 .Alternatively, the DST integrity processing unit 20 may be implementedas the DST execution unit 36. The DST client module 34 may beimplemented as the user device 12 or the DST processing unit 16 of FIG.1 .

The DST integrity processing unit 20 issues rebuilding access requestsvia the network 24 to the DST execution unit 36 to facilitate rebuildingthe one or more encoded data slices associated with a slice error. Therebuilding access requests include one or more of a list range request,a list digest of a range request, a read slice request, a write rebuiltslice request. Substantially simultaneously, the DST client module 34issues slice access requests via the network 24 to the DST executionunit 36 with regards to accessing encoded data slices stored in the DSTexecution unit 36. The slice access requests include at least one of aread request, a write request, a delete request, and a list request. Arate of the rebuilding access requests may be associated with acontrolled rate (e.g., by the DST integrity processing unit 20) ofrebuilding encoded data slices based on a rate of detecting the sliceerrors. A rate of the slice access requests may be associated with arate of accessing by a plurality of DSN users.

The DST execution unit 36 may be associated with an overall access rateto accommodate both the rebuilding access requests and the slice accessrequests. As such, the DST execution unit may accommodate morerebuilding access requests when there are fewer slice access requests ormay accommodate more slice access requests when there are fewerrebuilding access requests. Accordingly, when the DST integrityprocessing unit 20 establishes the rate for the rebuilding accessrequests, a resulting rate of slice access requests may be realized(e.g., roughly as a difference between the overall access rate minus theestablished rate for the rebuilding access requests).

The DST integrity processing unit 20 determines the rate for therebuilding access requests to achieve the desired rebuilding accessrequest rate and a resulting acceptable rate of the slice accessrequests. As an example, the DST integrity processing unit 20 detectsresulting slice access performance rates for a corresponding selectedrebuilding access performance rates to produce scoring information. Whenadjusting the rate for the rebuilding access request, the DST integrityprocessing unit selects the rate for the rebuilding access requestsbased on a desired rate of slice access requests in accordance with thescoring information. From time to time, the DST integrity processingunit 20 updates the scoring information based on observed rates of sliceaccess requests for corresponding selected rates for the rebuildingaccess requests. Such scoring information is discussed in greater detailwith reference to FIG. 9B.

FIG. 9B is a timing diagram illustrating an example 905 of accessperformance that includes a graphical indication of resulting sliceaccess performance levels (e.g. megabytes per second) for selectedrebuilding access performance levels (e.g., megabytes per second) for aseries of time intervals 1-8, and a resulting set of scores for the setof time intervals. The score may be generated based on a function ofslice access performance rate and slice rebuilding access rate. Forexample, the score may be calculated in accordance with a scoringformula: score=((3*rebuild rate)+access rate){circumflex over ( )}2.

The rebuilding rate is the rate at which data may be rebuilt from slicesby a processing device/system (data size/time). For a given selectedrebuilding rate (e.g., 8 MB/s), an associated score may be subsequentlyupdated in accordance with a learning rate function when an updatedcorresponding slice access rate is measured for the given selectedrebuilding rate. For example, the associated score may be subsequentlyupdated in accordance with a learning rate function formula of: updatedscore=(old score)*(1-learning rate)+(new score*learning rate). Forinstance, updated score=81=80*(1-0.1)+(90*0.1), when the learning rateis 10%, the old score is 80, and the new score is 90.

FIG. 9C is a flowchart illustrating an example of prioritizing accessrates. The method includes step 910 where a processing module (e.g., ofa distributed storage and task (DST) integrity processing unit) monitorsa slice access rate to produce an observed slice access rate for anassociated rebuilding rate of a set of rebuilding rates. The monitoringincludes at least one of performing a test, initiating a query, andreceiving access rate information.

The method continues at step 912 where the processing module applies alearning function to the observed slice access rate based on a previousobserved slice access rate associated with the rebuilding rate toproduce an updated previous observed slice access rate of a set ofprevious observed slice access rates, where the set of previous observedslice access rates corresponds to the set of rebuilding rates. Themethod continues at step 914 where the processing module updates a scoreassociated with the updated previous observed slice access rate and therebuilding rate.

In an example of updating a rebuilding rate, the method continues atstep 916 where the processing module determines to update the rebuildingrate for a storage unit. The determining may be based on one or more ofdetecting an end of a time interval, receiving a request, receiving anerror message, and detecting an unfavorable slice access rate. Themethod continues at step 918 where the processing module determinesslice access demand rate and rebuilding access demand rate. Thedetermining may be based on one or more of interpreting a queue,receiving a request, and accessing a historical record.

The method continues at step 920 where the processing module identifiesa prioritization scheme of one of a slice access priority scheme, acompromise scheme, and a rebuilding priory scheme. The identifying maybe based on one or more of a predetermination, detecting that a demandrate is much greater than a demand threshold level, and receiving arequest. For example, the processing module selects the slice accesspriority scheme when the slice access demand rate is much greater thanthe rebuilding access demand rate. As another example, the processingmodule selects the rebuilding priory scheme when the rebuilding accessdemand rate is much greater than the slice access demand rate. As yetanother example, the processing module selects the compromise schemewhen the slice access demand rate and the rebuilding access demand rateare similar.

When the processing module selects the compromise prioritization scheme,the method continues at step 922 where the processing module selects arebuilding rate of the set of rebuilding rates that is less than therebuilding access demand rate and maximizes a score associated with anexpected slice access rate. The selecting may be based on one or more ofaccessing a table, accessing a record, and calculating the rebuildingrate. When the processing module selects the slice access priorityscheme, the method continues at step 924 where the processing moduleselects the rebuilding rate of the set of rebuilding rates such that anestimated slice access rate is greater than the slice access demandrate. For example, the processing module selects the rebuilding ratefrom the scoring information such that the rebuilding rate is associatedwith a slice access rate that is greater than the slice demand rate.When the processing module selects the rebuilding priory scheme, themethod continues at step 926 where the processing module selects therebuilding rate of the set of rebuilding rates to be greater than therebuilding access demand rate. For example, the processing moduleselects the rebuilding rate to be just greater than a rebuilding rate ofthe scoring information. The method continues at step 930 where theprocessing module lowers the rebuilding rate when the estimated sliceaccess rate is not greater than a threshold. For example, the processingmodule determines the threshold based on a slice access demand rate anda minimum difference.

FIGS. 10A-B are diagrams illustrating examples 1000 and 1005 ofmodifying scoring information that includes scoring information at twotime frames. The scoring information includes an association of valuesof a set of rebuilding rates (RR), a set of slice access rates (SAR),and a set of scores (SCR) (e.g., score=((3*rebuild rate)+slice accessrate){circumflex over ( )}2). Initial scoring information is representedfor a time frame 10 and updated scoring information is represented for asubsequent timeframe 11. The updating of the scoring information isupdated in accordance with a score updating scheme.

In particular, FIG. 10A represents an example 1000 when the scoringupdating scheme includes updating slice access rates and scores when anobserved slice access rate is greater than a previous observed sliceaccess rate for a given rebuilding rate. For example, for a rebuildingrate of 8 MB per second, the observed slice access rate is 79 fortimeframe T 11 and the previous observed slice access rate is 40 MB persecond at timeframe T 10. The entry for the slice access ratecorresponding to the rebuilding rate of 8 MB per second is updated from40 MB per second to 79 MB per second. Accordingly, the score is updatedas well from 4,096 to 10,609. The slice access rate entries forrebuilding rates of 6 MB per second and 4 MB per second are also updatedto 79 MB per second since corresponding slice access rates at timeframeT 10 were less than 79 MB per second. Accordingly, scores associatedwith the rebuilding rates of 4 MB per second and 6 MB per second of T 11are updated.

FIG. 10B represents another example 1005 when the scoring updatingscheme includes updating slice access rates and scores when the observedslice access rate is less than the previous observed slice access ratefor the given rebuilding rate. For example, for a rebuilding rate of 6MB per second, the observed slice access rate is 7 for timeframe T 11and the previous observed slice access rate is 50 MB per second attimeframe T 10. The entry for the slice access rate corresponding to therebuilding rate of 6 MB per second is updated from 50 MB per second to 7MB per second. Accordingly, the score is updated as well from 4,624 to625. The slice access rate entries for rebuilding rates of 8 MB persecond, 12 MB per second, and 16 MB per second are also updated to 7 MBper second since corresponding slice access rates at timeframe T 10 weregreater than 7 MB per second. Accordingly, scores associated with therebuilding rates of 8 MB per second, 12 MB per second, and 16 MB persecond of T 11 are updated. The method of operation is discussed ingreater detail with reference to FIG. 10C.

FIG. 10C is a flowchart illustrating an example of updating scoringinformation, which includes similar steps to FIG. 9C. The method beginswith step 1010 where a processing module (e.g., of a distributed storageand task (DST) integrity processing unit) monitors a slice access rateto produce an observed slice access rate for an associated rebuildingrate of a set of rebuilding rates and applies a learning function to theobserved slice access rate to produce an updated previous observed sliceaccess rate as shown in step 1012. When the updated observed sliceaccess rate is greater than the previous observed slice access rate forthe rebuilding rate, the method continues to step 1014 where theprocessing module updates any remaining previous observed slice accessrates that are lower than the updated previous observed slice accessrate and are associated with another rebuilding rate that is less thanthe rebuilding rate (e.g., FIG. 10A example). When the updated observedslice access rate is less than the previous observed slice access ratefor the rebuilding rate, the method continues at the step 1016 where theprocessing module updates any remaining previous observed slice accessrates that are greater than the updated previous observed slice accessrate and are associated with another rebuilding rate that is greaterthan the rebuilding rate (e.g., FIG. 10B example). The method continuesat step 1018 where the processing module updates a score associated withthe updated previous observed slice access rate.

FIG. 11A is a diagram illustrating another example 1100 of modifyingscoring information that includes scoring information at three timeframes. The scoring information includes an association of values of aset of rebuilding rates (RR), a set of slice access rates (SAR), and aset of scores (SCR). Initial scoring information is represented for atime frame T 20 and updated scoring information is represented forsubsequent timeframes T 21 and T 22. The updating of the scoringinformation is updated in accordance with a score updating scheme, wherea formula to generate the score may be updated for each timeframe basedon rebuilding activity. The rebuilding activity may include scanningstorage of encoded data slices to detect one or more storage errorsassociated with the encoded data slices. A measure of rebuildingactivity includes identifying when a particular DSN address rangeassociated with the encoded data slices has been scanned for sliceerrors. Periodic scanning for errors may be desired to quickly identifyand resolve slice errors. As time goes on, and a particular DSN addressrange has not been scanned for errors, the formula to generate the scoremay be updated to facilitate a timelier scanning for slice errors.

In particular, the scoring information at timeframe T 20 may includegenerating the scores using a formula of: score=((rebuildrate){circumflex over ( )}2.5+(slice access rate){circumflex over( )}2.5). As such, similar priority is given to both rebuilding (e.g.,scanning) and slice access for routine reads and writes of data. As timegoes on, and the particular DSN address range has not been scanned, thescoring formula may be updated to a formula of: score=((rebuildrate){circumflex over ( )}3+(slice access rate){circumflex over ( )}2).As such, for higher priority is associated with rebuilding and lowerpriority is associated with slice access for the routine reads andwrites of the data. As time goes on, and the particular DSN addressrange has not been scanned, the scoring formula may be further updatedto a formula of: score=((rebuild rate){circumflex over ( )}3.5+(sliceaccess rate){circumflex over ( )}1.5). As such, an even higher priorityis associated with rebuilding and an even lower priority is associatedwith slice access for the routine reads and writes of the data. Once theparticular DSN address range has been scanned, the scoring formula maybe returned back to the initial formula: score=((rebuildrate){circumflex over ( )}2.5+(slice access rate){circumflex over( )}2.5) when the similar priority is desired. The method of operationis discussed in greater detail with reference to FIG. 11B.

FIG. 11B is a flowchart illustrating another example of updating scoringinformation, which includes similar steps to FIG. 9C. The methodincludes step 1110 where a processing module (e.g., of a distributedstorage and task (DST) integrity processing unit) determines to updatescoring information that includes a set of rebuilding rates, a set ofslice access rates, and a corresponding set of scores. The determiningmay be based on one or more of a time frame has elapsed since a lastupdate, interpreting a schedule, receiving an error message, anddetecting that a rate of rebuilding is less than a desired rate ofrebuilding (e.g., rebuilding is falling behind).

The method continues at step 1112 where the processing module determineswhether a dispersed storage network (DSN) address range associated withthe scoring information has been scanned since a last scoringinformation update. The determining may be based on one or more ofreceiving an error message, interpreting a schedule, initiating a query,and receiving a query response. When the processing module determinesthat the DSN address range associated with the scoring information hasnot been scanned since the last scoring information update, the methodbranches to step 1116 where the processing module biases for rebuilding.When the processing module determines that the DSN address rangeassociated with the scoring information has been scanned since the lastscoring information update, the method continues to step 1114. Themethod continues at step 1114 where the processing module uses defaultsfor an updating scoring function. For example, the processing moduleresets exponents on rebuilding rate and on slice access rate to defaultswithin a scoring formula. The method branches to step 1118 where theprocessing module updates the set of scores.

When the processing module determines that the DSN address rangeassociated with the scoring information has not been scanned since thelast scoring information update, the method continues at step 1116 wherethe processing module biases the rebuilding in the updated scoringfunction. For example, the processing module raises an exponent on therebuilding rate and lowers the exponent on the slice access rate of thescoring formula.

The method continues at step 1118 where the processing module updates aset of scores based on the updated scoring function. For example, theprocessing module calculates the scoring formula on the set of scoresusing the updated scoring function. The method continues with steps1120, 1122 and 1124, which are similar to steps 916, 918 and 922 of FIG.9C where the processing module determines to update a rebuilding ratefor a storage unit, determine slice access demand rate and rebuildingaccess demand rate, and selects a rebuilding rate of the set ofrebuilding rates that is less than the rebuilding access demand rate andmaximizes a score associated with an expected slice access rate.

FIG. 12A is a diagram of another example of updating scoring informationthat includes scoring information at three time frames. The scoringinformation includes an association of values of a set of rebuildingrates (RR), a set of input/output rates (TO), and a set of scores (SCR)(e.g., score=((3*rebuild rate)+slice access rate){circumflex over( )}2). In one embodiment, the IO rate refers to the rate at whichencoded data slices are accessed at a storage unit for read/writeoperations. In another embodiment, the set of IO rates includes theslice access rates (e.g., write/read operations) plus other DSNprocessing rates (e.g., rebuilding rates) for a set of storage units.Initial scoring information is represented for a time frame T 10 andupdated scoring information is represented for subsequent timeframes T11 and T 12. The updating of the scoring information is updated inaccordance with a score updating scheme.

In an example 1200, the scoring updating scheme includes updating IOrates and scores when an IO rate is greater than a threshold difference(e.g., more than, less than) an initial IO rate for a given rebuildingrate. In this example, a threshold difference is 3 MB/s for a rebuildingrate of 8 MB per second, the IO rate is 79 for timeframe T 11 and theinitial IO rate is 40 MB per second at timeframe T 10. As the thresholddifference is exceeded (e.g., 79−40=39>3), the entry for the IO ratecorresponding to the rebuilding rate of 8 MB per second is updated from40 MB per second to 79 MB per second. Accordingly, the score is updatedas well from 4,096 to 10,609. The IO rate entries for each rebuildingrate of the set of rebuilding rates are also updated (along with theircorresponding scores) according to one or more learning rate functions.

For example, the plurality of IO rates may all be updated according to alearning rate function of: updated IO rate=(initial IO rate)*(1−learningrate)+(IO rate*learning rate). In this example, the plurality of IOrates correspond to a set of storage units. Note the plurality of IOrates for the set may be based on a lowest IO rate of a storage unit ofthe set of storage units, an average IO rate of the set of storageunits, a mean IO rate, etc.).

As another example, each IO rate may be updated according to a differentlearning rate function. For example, a first learning rate function maybe applied to storage units storing a decode threshold number of encodeddata slices of a set of encoded data slices and a second learning ratefunction may be applied to storage units storing a redundancy number ofencoded data slices of the set of encoded data slices. By utilizingseparate learning rate functions, the score for the decode threshold maybe weighted so that storage units storing the decode threshold number ofencoded data slices will give higher priority to the rebuilding thanstorage units storing the redundancy number of encoded data slices. Themethod of operation is discussed in greater detail with reference toFIG. 12B.

FIG. 12B is a flowchart illustrating another example of updating scoringinformation. The method begins at step 1200, where a computing device ofa dispersed storage network (DSN) obtains scoring information for arebuilding for one or more storage units of a set of storage units ofthe DSN. The scoring information includes a plurality of rebuildingrates, a plurality of input/output rates, a plurality of scores, and aplurality of selection rates. In one embodiment, the term selection raterefers to historic use of a particular rebuilding rate for a set ofstorage units. Note the plurality of input/output rates may correspondto a set of storage units or to a storage unit of the set of storageunits. Further note that at a previous time, the computing devicecalculates an initial input/output rate for each rebuilding rate of theplurality of rebuilding rates to produce the plurality of initialinput/output rates.

The method continues with step 1202, where the computing devicedetermines, based on the scoring information, that a first input/outputrate of the plurality of input/output rates for a first rebuilding rateof the plurality of rebuilding rates exceeds a difference thresholdcompared to an initial first input/output rate for the first rebuildingrate. Note the initial first input/output rate has a corresponding firstscore and a first selection rate (e.g., historical data (e.g.,percentage of time the first rebuilding rate has been implemented for afirst storage unit)). As one example, the difference threshold mayindicate to change one or more input/output rates when the initial firstinput/output rate is exceeded by the first input/output rate by 10%. Asanother example, the difference threshold may indicate to change one ormore input/output rates when the initial first input/output rate isexceeded (e.g., greater than, less than) by the first input/output rateby 2 MB/s. As yet another example, the difference threshold may indicateto change one or more input/output rates when the initial firstinput/output rate is exceeded by the first input/output rate by anyamount.

The method continues with step 1204, where the computing device adjuststhe plurality of initial input/output rates based on the firstinput/output rate to produce a plurality of updated input/output rates.For example, the computing device may apply a learning rate function tothe plurality of initial input/output rates and the plurality ofinput/output rates to produce the plurality of updated input/outputrates. As another example, the computing device may apply a firstlearning rate function for the first rebuilding rate to a firstinput/output rate of the plurality of initial input/output rates toproduce a first updated input/output rate of the plurality of updatedinput/output rates and may apply a second learning rate function for asecond rebuilding rate to a second input/output rate of the plurality ofinitial input/output rates to produce a second updated input/output rateof the plurality of updated input/output rates.

The method continues with step 1206, where the computing devicegenerates an updated plurality of scores for the plurality of rebuildingrates based on the plurality of updated input/output rate. The methodcontinues with step 1208, where the computing device implements therebuilding in accordance with the updated plurality of scores. Note a DSprocessing unit may determine to use a highest score for the rebuildingor may determine to use a rebuild rate without the highest score when animportance of data factor outweighs the highest score. For example, forcritical data in time T12, the DS processing unit may choose a rebuildrate of 8 MB/s (score of 10609) instead of rebuild rate of 2 MB/s (scoreof 15876) to rebuild critical data. The importance of data factor mayalso include determining the set of storage units may maintain estimatedfuture TO operations with the higher rebuilding rate.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, audio, etc. any of which may generally be referred to as‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “configured to”, “operably coupled to”, “coupled to”, and/or“coupling” includes direct coupling between items and/or indirectcoupling between items via an intervening item (e.g., an item includes,but is not limited to, a component, an element, a circuit, and/or amodule) where, for an example of indirect coupling, the intervening itemdoes not modify the information of a signal but may adjust its currentlevel, voltage level, and/or power level. As may further be used herein,inferred coupling (i.e., where one element is coupled to another elementby inference) includes direct and indirect coupling between two items inthe same manner as “coupled to”. As may even further be used herein, theterm “configured to”, “operable to”, “coupled to”, or “operably coupledto” indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, and/or “processing unit” may be a singleprocessing device or a plurality of processing devices. Such aprocessing device may be a microprocessor, micro-controller, digitalsignal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing module, module, processing circuit,and/or processing unit. Such a memory device may be a read-only memory,random access memory, volatile memory, non-volatile memory, staticmemory, dynamic memory, flash memory, cache memory, and/or any devicethat stores digital information. Note that if the processing module,module, processing circuit, and/or processing unit includes more thanone processing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, and/or any device thatstores digital information. The memory device may be in a form a solidstate memory, a hard drive memory, cloud memory, thumb drive, servermemory, computing device memory, and/or other physical medium forstoring digital information.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by a computing device of astorage network comprises: obtaining, by the computing device, scoringinformation for a rebuilding of encoded data slices for one or morestorage units of a set of storage units of the storage network, whereinthe scoring information includes two or more of a plurality ofrebuilding rates, a plurality of input/output rates, a plurality ofscores, and a plurality of selection rates, wherein a selection rate ofthe plurality of selection rates includes a historic use of a particularrebuilding rate of the plurality of rebuilding rates for the set ofstorage units; determining, by the computing device, a rebuilding rateof the plurality of rebuilding rates to utilize for the rebuilding basedon the scoring information; and implementing, by the computing device,the rebuilding of the encoded data slices in accordance with therebuilding rate.
 2. The method of claim 1 further comprises: determiningbased on the scoring information, by the computing device, that a firstinput/output rate of the plurality of input/output rates for a firstrebuilding rate of the plurality of rebuilding rates exceeds adifference threshold compared to an initial first input/output rate forthe first rebuilding rate, wherein the initial first input/output ratehas a corresponding first score and a first selection rate of theplurality of selection rates; adjusting, by the computing device, aplurality of initial input/output rates based on the first input/outputrate to produce a plurality of updated input/output rates; andgenerating, by the computing device, an updated plurality of scores forthe plurality of rebuilding rates based on the plurality of updatedinput/output rates to produce updated scoring information.
 3. The methodof claim 2 further comprises: determining, by the computing device, anupdated rebuilding rate of the plurality of rebuilding rates based onthe updated scoring information; and implementing, by the computingdevice, the rebuilding of the encoded data slices in accordance with theupdated rebuilding rate.
 4. The method of claim 2, wherein the adjustingcomprises: applying a learning rate function to the plurality of initialinput/output rates and the plurality of input/output rates to producethe plurality of updated input/output rates.
 5. The method of claim 2,wherein the adjusting comprises: applying a first learning rate functionfor the first rebuilding rate to a first input/output rate of theplurality of initial input/output rates to produce a first updatedinput/output rate of the plurality of updated input/output rates; andapplying a second learning rate function for a second rebuilding rate toa second input/output rate of the plurality of initial input/outputrates to produce a second updated input/output rate of the plurality ofupdated input/output rates.
 6. The method of claim 2 further comprises:calculating, by the computing device, an initial input/output rate foreach rebuilding rate of the plurality of rebuilding rates to produce theplurality of initial input/output rates.
 7. The method of claim 1further comprises: a first plurality of input/output rates correspond toa first storage unit of the set of storage units; and a second pluralityof input/output rates correspond to a second storage unit of the set ofstorage units.
 8. The method of claim 1, wherein the plurality of scoresare based on a prioritization scheme of a plurality of prioritizationschemes, and wherein a first plurality of scores for first scoringinformation is based on a first prioritization scheme and a secondplurality of scores for second scoring information is based on a secondprioritization scheme.
 9. The method of claim 8, wherein the pluralityof prioritization schemes comprises: a slice access priority scheme; acompromise priority scheme; and a rebuilding priority scheme.
 10. Acomputing device of a storage network comprises: memory; an interface;and a processing module, wherein the processing module is operablycoupled to the memory and the interface, and wherein the processingmodule is operable to: obtain scoring information for a rebuilding ofencoded data slices for one or more storage units of a set of storageunits of the storage network, wherein the scoring information includestwo or more of a plurality of rebuilding rates, a plurality ofinput/output rates, a plurality of scores, and a plurality of selectionrates, wherein a selection rate of the plurality of selection ratesincludes a historic use of a particular rebuilding rate of the pluralityof rebuilding rates for the set of storage units; determine a rebuildingrate of the plurality of rebuilding rates to utilize for the rebuildingbased on the scoring information; and implement the rebuilding of theencoded data slices in accordance with the rebuilding rate.
 11. Thecomputing device of claim 10, wherein the processing module is furtheroperable to: determine based on the scoring information, that a firstinput/output rate of the plurality of input/output rates for a firstrebuilding rate of the plurality of rebuilding rates exceeds adifference threshold compared to an initial first input/output rate forthe first rebuilding rate, wherein the initial first input/output ratehas a corresponding first score and a first selection rate of theplurality of selection rates; adjust a plurality of initial input/outputrates based on the first input/output rate to produce a plurality ofupdated input/output rates; and generate an updated plurality of scoresfor the plurality of rebuilding rates based on the plurality of updatedinput/output rates to produce updated scoring information.
 12. Thecomputing device of claim 11, wherein the processing module is furtheroperable to: determine an updated rebuilding rate of the plurality ofrebuilding rates based on the updated scoring information; and implementthe rebuilding in accordance with the updated rebuilding rate.
 13. Thecomputing device of claim 11, wherein the processing module is furtheroperable to: perform the adjusting by applying a learning rate functionto the plurality of initial input/output rates and the plurality ofinput/output rates to produce the plurality of updated input/outputrates.
 14. The computing device of claim 11, wherein the processingmodule is further operable to perform the adjusting by: apply a firstlearning rate function for the first rebuilding rate to a firstinput/output rate of the plurality of initial input/output rates toproduce a first updated input/output rate of the plurality of updatedinput/output rates; and apply a second learning rate function for asecond rebuilding rate to a second input/output rate of the plurality ofinitial input/output rates to produce a second updated input/output rateof the plurality of updated input/output rates.
 15. The computing deviceof claim 11, wherein the processing module is further operable to:calculate an initial input/output rate for each rebuilding rate of theplurality of rebuilding rates to produce the plurality of initialinput/output rates.
 16. The computing device of claim 11, wherein theprocessing module is further operable to: a first plurality ofinput/output rates corresponding to a first storage unit of the set ofstorage units; and a second plurality of input/output ratescorresponding to a second storage unit of the set of storage units. 17.The computing device of claim 10, wherein the processing module isfurther operable to: calculate the plurality of scores based on aprioritization scheme of a plurality of prioritization schemes; andcalculate a first plurality of scores for first scoring informationbased on a first prioritization scheme and calculate a second pluralityof scores for second scoring information based on a secondprioritization scheme.
 18. The computing device of claim 17, wherein theplurality of prioritization schemes comprises: a slice access priorityscheme; a compromise priority scheme; and a rebuilding priority scheme.